Problem Solving Using Classical Planners

Papers from the 2012 AAAI Workshop

Classical planning has made huge advances in the last twenty years, leading to solvers able to create plans with thousands of actions for problems described by hundreds of propositions. Yet, the assumptions of classical planning (determinism, model completeness, etc.) are often criticized as being too restrictive to address "real" planning problems.
Recently many researchers have started to exploit the good performance of classical planners to solve a much wider range of problems that, although they may not appear to be "deterministic planning" problems, nevertheless fit within the classical planning model (that is, propositional description of a known state and goal, deterministic actions that modify a state). The approach typically consists of creating classical planning problems whose solution is directly or indirectly used to obtain a solution to the original problem. In this way, classical planners have been used for dealing with more expressive planning problems, including incomplete information, temporally extended goals and preferences, as well as to solve problems in bioinformatics (for example,. genome rearrangement and gene regulatory networks), and active diagnosis. In some cases, modifications of the classical planner may be necessary.